Abstract: Provided are a method and system for recommending content. The method and system enable a user to be given recommendations of contents similar to what he/she likes. The method for recommending content includes creating a user profile according to a predetermined model based on a user's reaction to the content, obtaining content features from one or more data sources, and creating a list of recommended contents according to a predetermined process based on the user profile and the content features. The system for recommending content includes a user profiling module that creates user profiles according to a predetermined model based on a user's reaction to the content, a digital television module that obtains content metadata from one or more data sources, and a content rating module that creates a list of recommended contents based on the user profile and the content metadata received from the user profiling module and the digital television module.

Abstract: Provided are a method and system for recommending content. The method and system enable a user to be given recommendations of contents similar to what he/she likes. The method for recommending content includes creating a user profile according to a predetermined model based on a user's reaction to the content, obtaining content features from one or more data sources, and creating a list of recommended contents according to a predetermined process based on the user profile and the content features. The system for recommending content includes a user profiling module that creates user profiles according to a predetermined model based on a user's reaction to the content, a digital television module that obtains content metadata from one or more data sources, and a content rating module that creates a list of recommended contents based on the user profile and the content metadata received from the user profiling module and the digital television module.